{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4c0e3a44-dfd3-4c38-ac5c-1d92941a3f91",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T02:24:00.228633Z",
     "iopub.status.busy": "2022-03-28T02:24:00.227596Z",
     "iopub.status.idle": "2022-03-28T02:24:00.234577Z",
     "shell.execute_reply": "2022-03-28T02:24:00.233730Z",
     "shell.execute_reply.started": "2022-03-28T02:24:00.228588Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2*3全1数组： \n",
      " [[1 1 1]\n",
      " [1 1 1]\n",
      " [1 1 1]\n",
      " [1 1 1]\n",
      " [1 1 1]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "ar1=np.ones((5,3),dtype=int)\n",
    "print(\"3*5全1数组：\",'\\n',ar1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "68e2811e-a3c7-400d-84ec-69fca99ace25",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T02:53:33.865382Z",
     "iopub.status.busy": "2022-03-28T02:53:33.864549Z",
     "iopub.status.idle": "2022-03-28T02:53:34.373888Z",
     "shell.execute_reply": "2022-03-28T02:53:34.372717Z",
     "shell.execute_reply.started": "2022-03-28T02:53:33.865344Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "生成二维随机数组： [[0.28383304 0.68179316]\n",
      " [0.58687421 0.75766052]\n",
      " [0.65081498 0.99022717]\n",
      " [0.83529009 0.9930423 ]\n",
      " [0.64098496 0.8020005 ]]\n",
      "[ 0.43048662 -1.0010831   1.64482368  1.27189642  0.56328347 -0.07901907\n",
      "  0.21311853  0.32470718  0.13174298  0.28613289 -0.18657288  0.60027576\n",
      " -1.74786052  0.68495939  0.43659118  0.79052721 -0.00598555  0.50732398\n",
      "  0.59825811  1.5296119   0.81490364  0.10369371 -0.99078898  0.1113961\n",
      " -0.17691745 -1.07629839  0.85944692 -0.92191308  0.46175644 -0.8842406\n",
      " -1.15975754  0.2587584   0.42622507 -0.83463894  0.98871656  0.04792957\n",
      "  0.15198128  0.65070972  0.55164112 -0.11096872 -0.94063776 -0.59730824\n",
      "  0.9571223   0.56899872 -2.37496795 -1.76712432  1.42537955  1.67973542\n",
      " -1.69690856 -0.19801719  1.21336936 -0.11363011 -0.98016568 -0.71051341\n",
      "  1.20375933 -1.81331347 -0.27929357 -0.79817907 -0.26579115 -0.15161779\n",
      "  0.06053586 -2.05701584 -0.17643968  0.57015629 -0.54424532 -0.01831292\n",
      " -1.91390134  0.6142717  -0.77482533 -0.44574487 -0.52645646  0.81693522\n",
      "  2.55210514 -0.69471113  1.0582845   0.99808599  0.38571388  0.52937141\n",
      "  0.66021156  1.30110095 -1.03784046  0.36086086 -0.10988815  0.00290702\n",
      "  0.80373175  0.72596849 -0.06959716  0.89895458  0.50148734  2.39083986\n",
      " -1.04537761  0.4502733   0.54888669 -1.21662576 -0.9206598   1.59542711\n",
      " -1.40565334  0.35655451  0.10594569 -0.17398963]\n",
      "生成1-10（不包括10）的10个随机整数： [50  9 89 25 24 67 62 94 87 68 26 16 10  9 59  5 31 35 78 97 13  6 38 28\n",
      "  3  1 40 74 14 69 83 83 87 19 51 96 27 50 59  7  3 21 61 32 51 83  3 37\n",
      " 84 59 97 20 72 80  2 33 71 50 65 92 77 45  3 82 99 62 35 99  8 26 52 16\n",
      " 34 43 98 40 12 57 22 28 28  5  6 82 85 84 42 99 56  9  4 84 92  6 30 36\n",
      " 72 44 17  2]\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'n4' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_2238/283466582.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0max3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0max3\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0max3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'n4' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np \n",
    "n=np.random.rand(5,2)\n",
    "print(\"生成二维随机数组：\",n)\n",
    "n1=np.random.randn(100)\n",
    "print(n1)\n",
    "import matplotlib.pyplot as plt \n",
    "fig,axes=plt.subplots(2,2)\n",
    "ax1=axes[0,0]\n",
    "ax1.hist(n1)\n",
    "ax1.grid()\n",
    "n2=np.random.randint(1,100,100)\n",
    "print(\"生成1-10（不包括10）的10个随机整数：\",n2)\n",
    "n3=np.random.normal(0,10,1000)\n",
    "ax2=axes[0,1]\n",
    "ax2.hist(n3)\n",
    "ax2.grid()\n",
    "ax3=axes[1,0]\n",
    "ax3.plot(n2)\n",
    "ax3=axes[1,1]\n",
    "ax3.plot(n4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "65afc7fa-d1a6-45fd-9f5a-4c6e849b9976",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-04T02:10:42.781589Z",
     "iopub.status.busy": "2022-04-04T02:10:42.781068Z",
     "iopub.status.idle": "2022-04-04T02:10:42.788024Z",
     "shell.execute_reply": "2022-04-04T02:10:42.787386Z",
     "shell.execute_reply.started": "2022-04-04T02:10:42.781555Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "生成1-15（不包括15）的15个随机整数： [12  3  9  6 13  1  5 10  7  6  2  6  8  3  5]\n",
      "对数组进行切片,间隔一个元素： [12  6  5  6  8] [ 3 13 10  2  3] [9 1 7 6 5]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \r\n",
    "n=np.random.randint(1,15,15)\r\n",
    "print(\"生成1-15（不包括15）的15个随机整数：\",n)\r\n",
    "n[2:5]\r\n",
    "print(\"对数组进行切片,间隔一个元素：\",n[0:15:3],n[1:15:3],n[2:15:3])\r\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "afd82299-4e6e-4669-bab8-ea042324215e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-04T02:44:16.563051Z",
     "iopub.status.busy": "2022-04-04T02:44:16.562603Z",
     "iopub.status.idle": "2022-04-04T02:44:17.212939Z",
     "shell.execute_reply": "2022-04-04T02:44:17.212299Z",
     "shell.execute_reply.started": "2022-04-04T02:44:16.563015Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分班成绩排序： [[90 90 90 90 90 90 91 91 91 91 91 91 91 92 92 92 92 93 93 93 94 94 94 94\n",
      "  94 94 94 95 95 96 96 96 96 96 96 96 96 97 97 97 97 98 98 98 98 98 98 98\n",
      "  98 98 98 98 98 99 99 99 99 99 99 99]\n",
      " [90 90 90 90 90 90 90 90 90 90 90 90 90 91 91 91 91 91 91 91 91 92 92 92\n",
      "  92 92 92 92 93 93 93 93 93 93 94 94 95 95 95 95 95 95 96 96 96 96 96 96\n",
      "  96 97 98 98 99 99 99 99 99 99 99 99]]\n",
      "将多维成绩数组变更为一维数组，并排序： [90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 91 91 91 91 91\n",
      " 91 91 91 91 91 91 91 91 91 91 92 92 92 92 92 92 92 92 92 92 92 93 93 93\n",
      " 93 93 93 93 93 93 94 94 94 94 94 94 94 94 94 95 95 95 95 95 95 95 95 96\n",
      " 96 96 96 96 96 96 96 96 96 96 96 96 96 96 97 97 97 97 97 98 98 98 98 98\n",
      " 98 98 98 98 98 98 98 98 98 99 99 99 99 99 99 99 99 99 99 99 99 99 99 99]\n",
      "两个班总的成绩平均值为： 94.26666666666667 标准差为： 3.156298817004217 方差为： 9.962222222222222 最高分为： 99 最低分为： 90 中位数为： 94.0\n",
      "两个班总的成绩平均值为(四舍五入）： 94.0 标准差为(向上取整）： 4.0 方差为（向下取整）： 9.0 最高分为： 99 最低分为： 90 中位数为： 94.0\n",
      "每一个班的最高成绩： [99 99]\n",
      "每一个班的平均成绩： [94.95       93.58333333]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np \r\n",
    "np.random.seed(10)\r\n",
    "stu=np.random.randint(90,100,120).reshape(2,60)\r\n",
    "print(\"分班成绩排序：\",np.sort(stu))\r\n",
    "stu_all=stu.flatten()\r\n",
    "print(\"将多维成绩数组变更为一维数组，并排序：\",np.sort(stu_all))\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "plt.style.use(\"ggplot\")\r\n",
    "fig,axes=plt.subplots(2,2)\r\n",
    "fig.set_size_inches(12,12)\r\n",
    "ax1=axes[0,0]\r\n",
    "ax1.hist(stu_all)\r\n",
    "ax2=axes[0,1]\r\n",
    "ax2.boxplot(stu_all)\r\n",
    "def descStu(arr):\r\n",
    "    avg=np.mean(arr)\r\n",
    "    std=np.std(arr)\r\n",
    "    var=np.var(arr)\r\n",
    "    max_s=np.max(arr)\r\n",
    "    min_s=np.min(arr)\r\n",
    "    med_s=np.median(arr)\r\n",
    "    return avg,std,var,max_s,min_s,med_s\r\n",
    "avg_s,std_s,var_s,max_s,min_s,med_s=descStu(stu)\r\n",
    "\r\n",
    "print(\"两个班总的成绩平均值为：\",avg_s,\"标准差为：\",std_s,\"方差为：\",var_s,\"最高分为：\",max_s,\"最低分为：\",min_s,\"中位数为：\",med_s)\r\n",
    "print(\"两个班总的成绩平均值为(四舍五入）：\",np.rint(avg_s),\"标准差为(向上取整）：\",np.ceil(std_s),\"方差为（向下取整）：\",np.floor(var_s),\"最高分为：\",max_s,\"最低分为：\",min_s,\"中位数为：\",med_s)\r\n",
    "print(\"每一个班的最高成绩：\",stu.max(axis=1))\r\n",
    "print(\"每一个班的平均成绩：\",stu.mean(axis=1))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
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